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Anti-Caldesmon antibody [ABT-CALD]

Catalog No : V0012
  • Applications
    IHC, WB
  • Host Species
    Mouse
  • Reactivity
    Human
  • Size
    50ul
  • Price
    $175.00
Details
  • Product Name
    Anti-Caldesmon antibody [ABT-CALD]
  • Catalog No
    V0012
  • Description
    Mouse monoclonal [ABT-CALD] antibody to CALD
  • Applications
    IHC, WB
  • Clonality
    Monoclonal
  • Host Species
    Mouse
  • Reactivity
    Human
  • ABT Clone ID
    ABT-CALD
  • Antigen Retrieval
    Heat-induced epitope retrieval (HIER)
  • Cell Marque/Ventana Control
    Appendix, breast
  • Concentration
    1mg/ml
  • Dilution
    1:50-200
  • Entrez Gene ID (NCBI)
    800
  • IHC Recommended control
    Normal Colon, Liomyoma
  • IHC Target Name
    Caldesmon
  • Immunogen
    Synthetic peptide
  • Localization
    Cytoplasmic
  • OMIM
    114213
  • Purification
    Immunogen affinity purified
  • Research
    Small, round blue cell tumors: leiomyosarcoma, PEComa: angiomyolipoma, lymphangiomyomatosis, Spingle cell tumors: myofibroblastic tumors, smooth muscle
  • Retrieval buffer
    TRIS-EDTA of pH8.0
  • Source/Ig Isotype
    Mouse IgG
  • Storage/Stablility
    -20°C/1 year
  • Synonyms
    CAD, CALD1, CDM, L-caldesmon, Non-muscle caldesmon
  • UniProt Gene Name
    CALD1
  • UniProtKB
    Q05682
  • Volume
    2ml
Application Images
Image 1
Image 2
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
For the process of attaching edges to nodes, it is straightforward to compute the likelihood using (2). However, because of the nature of weighted sampling without replacement, we have to, for each i, calculate the probability conditi onal on each of the Xi!Xi! permutations of the selected nodes and then aver age over all Xi!Xi! probabilities to arrive at the likelihood. As calculating the exact likelihood in this way is not computationally feasible because the factorial grows faster than the exponential function, we approximate the likelihood based on one permutation of weighted sampling without replacement instead. The contribution by the new edges brought by node i is
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